Unveiling Social Belonging: Exploring the Narratives of Immigrant Muslim Older Women
Bibliographic record
Abstract
Background and Objectives. Older adults who lack a secure sense of social belonging may report loneliness, isolation, and ostracization in their communities. Little attention has been paid to the perceptions of social belonging among immigrant Muslim older (IMO) women. This study aimed to address this gap by exploring IMO women’s experiences of social belonging. Research Design and Methods. This qualitative descriptive study used photo elicitation and narrative interviewing to draw on the experiences of 14 IMO women living in Edmonton, Canada. An integrative framework of social belonging was used to guide theoretical conceptualizations of what comprises belonging, and a thematic analysis approach was used to highlight factors and influences that shape how participants have constructed their experiences of belonging. Results. The findings suggest that a sense of belonging is influenced by feelings of loneliness and loss, opportunities for community engagement, and social competencies related to maintaining family relationships. Additionally, the findings indicate the importance of IMO women’s perceptions and reflections on aging as these shape their sense of belonging. These findings not only provide insight into the intricate and shifting nature of belonging but also emphasize the need for structural support to benefit both IMO women and the communities they reside in. Discussion and Implications. Cultivating belonging is a collective responsibility involving older women, their social networks, and society at large, including government and public services. A sense of belonging is crucial to counter ageism and promote positive self‐perceptions of aging, particularly within ethnocultural communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".